Ron Mitchell

Summary AI “agents” — autonomous, task-focused software that can read, act, and learn across systems — are moving from tech demos into real business use. Modern agent platforms let teams connect CRMs, calendars, ticketing systems, and data warehouses and then automate end-to-end work: qualifying leads, creating sales forecasts, generating recurring reports, escalating support cases, and […]

Quick summary AI agents — autonomous software that can read, act, and follow up across apps — are moving from pilots into real business use. Teams are now using agents to qualify leads, generate routine reports, automate order follow-ups, and handle first-line customer inquiries. Instead of a human doing every repetitive step, an agent can

The story (short) Over the past year we’ve seen autonomous AI agents — systems that can carry out multi-step tasks with little human prompting — move out of labs and into real business workflows. Companies are using agents to draft and send outreach, triage customer requests, update CRMs, summarize meetings, and generate recurring reports. These

Quick summary AI agents — autonomous, multi-step AI systems that can search, summarize, act, and update systems — are no longer just prototypes. Businesses are putting them into production to handle things like lead outreach, CRM updates, automated reporting, and routine approvals. The underlying pattern is simple: combine retrieval-augmented generation (RAG) or vector search for

Quick summary This year businesses have accelerated pilots of autonomous AI agents — software “team members” powered by large language models that can take actions (triage leads, schedule meetings, run reports, open tickets) rather than just answer questions. Advances in model capability, easier integrations, and low-code agent toolkits mean these agents are now practical for

Summary AI agents — autonomous apps that can read data, take actions, and learn from outcomes — have crossed a practical threshold in 2025. Tools and frameworks are now mature enough for reliable, repeatable deployments: agents can draft and personalize sales outreach, run recurring financial and operational reports, trigger inventory reorders, and even handle parts

Quick summary AI agents—autonomous, LLM-driven assistants that can read data, call APIs, and take actions—are shifting from experiments to real business tools. Companies are now using them to qualify leads, update CRMs, generate sales reports, automate procurement steps, and triage customer issues. Advances in retrieval-augmented generation (RAG), secure API access, and orchestration platforms make these

Short summary AI “agents” — software that can act autonomously, use tools, and connect to your systems — are moving from demos into real business work. These agents can qualify leads, update CRMs, generate pitch decks, pull and summarize sales reports, and trigger follow-up actions without a human typing every step. The result: faster cycles,

Quick summary AI agents — customizable, task-focused AI assistants that can read your systems, act on your behalf, and learn from feedback — have moved from lab experiments into real business use. Companies are using them to qualify leads, automate routine outreach, generate weekly sales reports, and trigger workflows across CRM, calendar, and ticketing systems.

Quick summary AI “agents” — autonomous systems that can take multi-step actions (write outreach, book meetings, pull reports, close simple tickets) — have moved from lab demos to real business pilots. Advances in models, retrieval-augmented systems, and low-code agent orchestration mean companies can safely automate tasks that used to need routine human attention. Why this